At the beginning of this century, which is characterized by huge flows of emerging data, Dai
and Liao proposed a pervasive conjugacy condition that triggered the interest of many …

Minimizing two different upper bounds of the matrix which generates search directions of the
nonlinear conjugate gradient method proposed by Dai and Liao, two modified conjugate …

Based on an eigenvalue study, a descent class of Dai–Liao conjugate gradient methods is
proposed. An interesting feature of the proposed class is its inclusion of the efficient …

In this paper, we propose a Dai–Liao (DL) conjugate gradient method for solving large-scale
system of nonlinear equations. The method incorporates an extended secant equation …

The BFGS method is the most effective of the quasi-Newton methods for solving
unconstrained optimization problems. Wei, Li, and Qi [16] have proposed some modified …

Convergence analysis of a modified BFGS method on convex minimizations Page 1
Comput Optim Appl (2010) 47: 237–255 DOI 10.1007/s10589-008-9219-0 Convergence …

The conjugate gradient (CG) method is one of the most popular methods for solving smooth
unconstrained optimization problems due to its simplicity and low memory requirement …

In this paper, a Riemannian BFGS method for minimizing a smooth function on a
Riemannian manifold is defined, based on a Riemannian generalization of a cautious …

The introduction of quasi-Newton and nonlinear conjugate gradient methods revolutionized
the field of nonlinear optimization. The self-scaling memoryless Broyden–Fletcher–Goldfarb …

A BFGS method, in association with a new backtracking line search technique, is presented
for solving symmetric nonlinear equations. The global and superlinear convergences of the …